Network delivered services and applications often benefit from generalities which can be associated with end-users, for example the end-user's current location. This information is used to alter the service in order to achieve a more relevant, higher performing, and ultimately more valuable experience for the end-user.
In order to obtain this information, some services require that an end-user perform a self-identification process, for example manually entering their current location or enabling their device to provide location information on their behalf. The former is a direct inconvenience for the end-user as they are actively involved in improving the service they are consuming. The latter is an indirect inconvenience, but one that also has implications with personal privacy as often times a device configured to provide information on behalf of the end-user, like current location, will do so globally to all services including malicious ones.
Alternatively, some services derive this information based on personally identifiable aspects of the end-users interaction with the service, for example the originating internet address of a request. When the identifiable aspects are not otherwise obfuscated, the service receives data that is too specific to use directly as a generalization and must rely on a mapping from high frequency specific information to useful general information, for example using a database to map one of the ˜4.3 billion possible IPv4 internet addresses to a significantly smaller set of geographical regions. Due to the key-space size of these databases, or number of possible IPv4 addresses that identify a single geographical region, the effort required to maintain an accurate result is extremely high as the information scales into larger datasets.
Furthermore, end-users can obfuscate personally identifiable aspects of an interaction by connecting to the service through intermediaries, such as web proxies, or other active methods of obfuscation. Maintaining the ability for an end-user to obfuscate personal data is desirable for personal privacy, however, it effectively defeats systems intended to derive generalized information, which would not otherwise raise privacy concerns from the hidden specific and personally identifiable information.
As a result, there is a great need for a system which can associate general information with an end-user in a way that neither burdens the end-user nor exposes personally identifiable information, and is less costly than maintaining a high-frequency mapping as the associated information scales to larger datasets.